Is unified memory on MacBook Pro M4 32GB as fast as VRAM for stablelm 2 zephyr 1.6b?

Not always. MacBook Pro M4 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

Can stablelm 2 zephyr 1.6b run on MacBook Pro M4 32GB?

YES — Runs Great

C42Usable

Estimated — low-sample bucket· few comparable runs

stablelm 2 zephyr 1.6b needs ~5.5 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 5.5 GB, 22.4 tok/s, Runs well

5.5 GB required23.0 GB available

24% VRAM used

Fit status

Runs well

Decode

22.4 tok/s

TTFT

8643 ms

Safe context

1.5M

Memory

5.5 GB / 23.0 GB

Memory breakdown

Weights1.0 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsstablelm 2 zephyr 1.6b on MacBook Pro M4 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 22.4 tok/s decode · 8.6s TTFT (warm) · 56 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	22.4 tok/s	4714 ms	1.4M
Coding	C	Runs well	22.4 tok/s	8643 ms	1.5M
Agentic Coding	C	Runs well	22.4 tok/s	12571 ms	1.5M
Reasoning	C	Runs well	22.4 tok/s	10214 ms	1.5M
RAG	C	Runs well	22.4 tok/s	15714 ms	1.5M

Inference speed

stablelm 2 zephyr 1.6b inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for stablelm 2 zephyr 1.6b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~30 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	30.4	Fits
RTX 4090 24GB	24 GB	Q4_K_M	25.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	25.6

Quantization options

How stablelm 2 zephyr 1.6b (1.600000023841858B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C43
Q3_K_S	3	0.8 GB	Low	C43
NVFP4	4

Get started

Copy-paste commands to run stablelm 2 zephyr 1.6b on your machine.

Run

lms load hf-second-state--stablelm-2-zephyr-1-6b-gguf && lms server start

Frequently asked questions

Can MacBook Pro M4 32GB run stablelm 2 zephyr 1.6b?

Yes, MacBook Pro M4 32GB can run stablelm 2 zephyr 1.6b with a C grade (Runs well). Expected decode speed: 22.4 tok/s.

How much VRAM does stablelm 2 zephyr 1.6b need?

stablelm 2 zephyr 1.6b (1.600000023841858B parameters) requires approximately 5.5 GB of memory with Q4_K_M quantization.

What is the best quantization for stablelm 2 zephyr 1.6b?

The recommended quantization for stablelm 2 zephyr 1.6b is Q4_K_M, which balances quality and memory efficiency.

What speed will stablelm 2 zephyr 1.6b run at on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, stablelm 2 zephyr 1.6b achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8643ms using Q4_K_M quantization.

Can MacBook Pro M4 32GB run stablelm 2 zephyr 1.6b for coding?

For coding workloads, stablelm 2 zephyr 1.6b on MacBook Pro M4 32GB receives a C grade with 22.4 tok/s and 1.5M context.

What context window can stablelm 2 zephyr 1.6b use on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, stablelm 2 zephyr 1.6b can safely use up to 1.5M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 32GB See all hardware for stablelm 2 zephyr 1.6b

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